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A Time Series Data Prediction Scheme Using Bilinear Recurrent Neural Network

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1 Author(s)
Dong-Chul Park ; Dept. of Electron. Eng., Myong Ji Univ., Yong In, South Korea

A time series prediction method based on a BiLinear Recurrent Neural Network (BLRNN) is proposed in this paper. The proposed predictor is based on the BLRNN that has been proven to have robust abilities in modeling and predicting time series. The learning process is further improved by using a multiresolution-based learning algorithm for training the BLRNN so as to make it more robust for the prediction of time series data. The proposed multiresolution-based BLRNN predictor is applied to the long-term prediction of time series data sets. Experiments and results on the Mackey-Glass Series data and Sunspot Series data show that the proposed prediction scheme outperforms both the traditional MultiLayer Perceptron Type Neural Network (MLPNN) and the BLRNN in terms of the normalized mean square error (NMSE).

Published in:

2010 International Conference on Information Science and Applications

Date of Conference:

21-23 April 2010